New Algorithms for Visual Data Mining
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 15476
Special Issue Editor
Special Issue Information
Dear Colleagues,
In current times, scientific, industrial and societal developments rely heavily on data collection and understanding. Computational approaches are paramount to support such activities and the expectation is that the demand for reliable approaches to working with complex data will continue to increase.
Challenges in Data Science and Analytics have highlighted many circumstances where Data Mining, Machine Learning and Statistics methods need to be paired with strong user engagement to support exploratory data analysis as well as illustrative, demonstrative and data story telling tasks. The fields of Data Visualization and Visual Analytics have thrived in this scenario, in the effort to provide support for understanding and explaining data and to support building and applying data models in a very extensive variety of applications.
This Special Issue calls for novel contributions in the development of algorithms and techniques that combine Data Mining (DM) and Machine Learning (ML) algorithms and strategies with Visual Layouts and interaction to support user engagement in any part of the processes of Data Science.
Papers on novel approaches and algorithms are welcome in subjects related to Visual Data Mining and applications. Target subjects include, but are not limited to the following:
- Machine Learning approaches adapted to user engagement.
- User-centered machine learning
- Visual Feedback in data mining and data analysis
- Visual Clustering and Cluster Analysis
- Visual Classification
- Visual Regression
- Visual approaches to data exploratory analysis supported by ML and DM algorithms.
- Visual learning approaches for attribute analysis and selection.
- Visual learning approaches to data labeling and annotation.
- Visual and ML approaches to data retrieval.
- ML and DM algorithms in support to Data Visualization
- Visual strategies for interpretation of Machine Learning methods.
- Visual Mappings for interpretation of multi-dimensional data, dimension reduction strategies and embeddings.
- Combined Point-based and Attribute-based Visualizations.
- Applications of Visual Data Mining in science, technology and industry, such as text and image mining, drug development, disease understanding, diagnosis and prognosis, physics, chemistry, biology and other scientific fields, social networks, news and fake news, monitoring of natural environments, etc..
- Design issues for Visual Data Mining Tools.
- Time related and Incremental Visual Learning.
Dr. Rosane Minghim
Guest Editor
Manuscript Submission Information
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